248 research outputs found
A Location-Sentiment-Aware Recommender System for Both Home-Town and Out-of-Town Users
Spatial item recommendation has become an important means to help people
discover interesting locations, especially when people pay a visit to
unfamiliar regions. Some current researches are focusing on modelling
individual and collective geographical preferences for spatial item
recommendation based on users' check-in records, but they fail to explore the
phenomenon of user interest drift across geographical regions, i.e., users
would show different interests when they travel to different regions. Besides,
they ignore the influence of public comments for subsequent users' check-in
behaviors. Specifically, it is intuitive that users would refuse to check in to
a spatial item whose historical reviews seem negative overall, even though it
might fit their interests. Therefore, it is necessary to recommend the right
item to the right user at the right location. In this paper, we propose a
latent probabilistic generative model called LSARS to mimic the decision-making
process of users' check-in activities both in home-town and out-of-town
scenarios by adapting to user interest drift and crowd sentiments, which can
learn location-aware and sentiment-aware individual interests from the contents
of spatial items and user reviews. Due to the sparsity of user activities in
out-of-town regions, LSARS is further designed to incorporate the public
preferences learned from local users' check-in behaviors. Finally, we deploy
LSARS into two practical application scenes: spatial item recommendation and
target user discovery. Extensive experiments on two large-scale location-based
social networks (LBSNs) datasets show that LSARS achieves better performance
than existing state-of-the-art methods.Comment: Accepted by KDD 201
Find a Reasonable Ending for Stories: Does Logic Relation Help the Story Cloze Test?
Natural language understanding is a challenging problem that covers a wide
range of tasks. While previous methods generally train each task separately, we
consider combining the cross-task features to enhance the task performance. In
this paper, we incorporate the logic information with the help of the Natural
Language Inference (NLI) task to the Story Cloze Test (SCT). Previous work on
SCT considered various semantic information, such as sentiment and topic, but
lack the logic information between sentences which is an essential element of
stories. Thus we propose to extract the logic information during the course of
the story to improve the understanding of the whole story. The logic
information is modeled with the help of the NLI task. Experimental results
prove the strength of the logic information.Comment: Student Abstract in AAAI-201
Personalized Video Recommendation Using Rich Contents from Videos
Video recommendation has become an essential way of helping people explore
the massive videos and discover the ones that may be of interest to them. In
the existing video recommender systems, the models make the recommendations
based on the user-video interactions and single specific content features. When
the specific content features are unavailable, the performance of the existing
models will seriously deteriorate. Inspired by the fact that rich contents
(e.g., text, audio, motion, and so on) exist in videos, in this paper, we
explore how to use these rich contents to overcome the limitations caused by
the unavailability of the specific ones. Specifically, we propose a novel
general framework that incorporates arbitrary single content feature with
user-video interactions, named as collaborative embedding regression (CER)
model, to make effective video recommendation in both in-matrix and
out-of-matrix scenarios. Our extensive experiments on two real-world
large-scale datasets show that CER beats the existing recommender models with
any single content feature and is more time efficient. In addition, we propose
a priority-based late fusion (PRI) method to gain the benefit brought by the
integrating the multiple content features. The corresponding experiment shows
that PRI brings real performance improvement to the baseline and outperforms
the existing fusion methods
Defense Against Model Extraction Attacks on Recommender Systems
The robustness of recommender systems has become a prominent topic within the
research community. Numerous adversarial attacks have been proposed, but most
of them rely on extensive prior knowledge, such as all the white-box attacks or
most of the black-box attacks which assume that certain external knowledge is
available. Among these attacks, the model extraction attack stands out as a
promising and practical method, involving training a surrogate model by
repeatedly querying the target model. However, there is a significant gap in
the existing literature when it comes to defending against model extraction
attacks on recommender systems. In this paper, we introduce Gradient-based
Ranking Optimization (GRO), which is the first defense strategy designed to
counter such attacks. We formalize the defense as an optimization problem,
aiming to minimize the loss of the protected target model while maximizing the
loss of the attacker's surrogate model. Since top-k ranking lists are
non-differentiable, we transform them into swap matrices which are instead
differentiable. These swap matrices serve as input to a student model that
emulates the surrogate model's behavior. By back-propagating the loss of the
student model, we obtain gradients for the swap matrices. These gradients are
used to compute a swap loss, which maximizes the loss of the student model. We
conducted experiments on three benchmark datasets to evaluate the performance
of GRO, and the results demonstrate its superior effectiveness in defending
against model extraction attacks
GAG: Global Attributed Graph Neural Network for Streaming Session-based Recommendation
Streaming session-based recommendation (SSR) is a challenging task that
requires the recommender system to do the session-based recommendation (SR) in
the streaming scenario. In the real-world applications of e-commerce and social
media, a sequence of user-item interactions generated within a certain period
are grouped as a session, and these sessions consecutively arrive in the form
of streams. Most of the recent SR research has focused on the static setting
where the training data is first acquired and then used to train a
session-based recommender model. They need several epochs of training over the
whole dataset, which is infeasible in the streaming setting. Besides, they can
hardly well capture long-term user interests because of the neglect or the
simple usage of the user information. Although some streaming recommendation
strategies have been proposed recently, they are designed for streams of
individual interactions rather than streams of sessions. In this paper, we
propose a Global Attributed Graph (GAG) neural network model with a Wasserstein
reservoir for the SSR problem. On one hand, when a new session arrives, a
session graph with a global attribute is constructed based on the current
session and its associate user. Thus, the GAG can take both the global
attribute and the current session into consideration to learn more
comprehensive representations of the session and the user, yielding a better
performance in the recommendation. On the other hand, for the adaptation to the
streaming session scenario, a Wasserstein reservoir is proposed to help
preserve a representative sketch of the historical data. Extensive experiments
on two real-world datasets have been conducted to verify the superiority of the
GAG model compared with the state-of-the-art methods
A Multi-Strategy based Pre-Training Method for Cold-Start Recommendation
Cold-start problem is a fundamental challenge for recommendation tasks. The
recent self-supervised learning (SSL) on Graph Neural Networks (GNNs) model,
PT-GNN, pre-trains the GNN model to reconstruct the cold-start embeddings and
has shown great potential for cold-start recommendation. However, due to the
over-smoothing problem, PT-GNN can only capture up to 3-order relation, which
can not provide much useful auxiliary information to depict the target
cold-start user or item. Besides, the embedding reconstruction task only
considers the intra-correlations within the subgraph of users and items, while
ignoring the inter-correlations across different subgraphs. To solve the above
challenges, we propose a multi-strategy based pre-training method for
cold-start recommendation (MPT), which extends PT-GNN from the perspective of
model architecture and pretext tasks to improve the cold-start recommendation
performance. Specifically, in terms of the model architecture, in addition to
the short-range dependencies of users and items captured by the GNN encoder, we
introduce a Transformer encoder to capture long-range dependencies. In terms of
the pretext task, in addition to considering the intra-correlations of users
and items by the embedding reconstruction task, we add embedding contrastive
learning task to capture inter-correlations of users and items. We train the
GNN and Transformer encoders on these pretext tasks under the meta-learning
setting to simulate the real cold-start scenario, making the model easily and
rapidly being adapted to new cold-start users and items. Experiments on three
public recommendation datasets show the superiority of the proposed MPT model
against the vanilla GNN models, the pre-training GNN model on user/item
embedding inference and the recommendation task
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